The Experts below are selected from a list of 4305 Experts worldwide ranked by ideXlab platform
Rui Zhang - One of the best experts on this subject based on the ideXlab platform.
-
node placement and distributed magnetic beamforming optimization for wireless Power transfer
IEEE Transactions on Signal and Information Processing over Networks, 2018Co-Authors: Mohammad Vedady R Moghadam, Rui ZhangAbstract:In multiple-input single-output (MISO) wireless Power transfer (WPT) via magnetic resonant coupling, multiple transmitters are deployed to enhance the efficiency of Power transfer to a single receiver by jointly adapting their source currents/voltages so as to constructively combine the induced magnetic fields at the receiver, a technique known as magnetic beamforming . In practice, since the transmitters (Power chargers) are usually at fixed locations and the receiver (e.g., mobile phone) is desired to be freely located in a target region for wireless charging, its received Power can fluctuate significantly over locations even with adaptive magnetic beamforming applied. To achieve uniform Power coverage, the transmitters need to be optimally placed in the region such that a minimum charging Power can be achieved for the receiver regardless of its location, which motivates this paper. First, we derive the optimal magnetic beamforming solution in closed form for a distributed MISO WPT system with fixed locations of the transmitters and receiver to maximize the deliverable Power to the receiver subject to a given Sum-Power Constraint at all transmitters. By applying adaptive magnetic beamforming based on this optimal solution, we then jointly optimize the locations of all transmitters to maximize the minimum Power deliverable to the receiver over a given one-dimensional (1D) region. Although the formulated problem is nonconvex, we propose an iterative algorithm for solving it efficiently. Extensive simulation results are provided that show the significant performance gains by the proposed design with optimized transmitter locations and magnetic beamforming as compared to other benchmark schemes with nonadaptive and/or heuristic transmitter current allocation and node placement. Last, we extend the node placement problem to the case of 2D region, and propose efficient designs for this case.
-
node placement and distributed magnetic beamforming optimization for wireless Power transfer
arXiv: Systems and Control, 2016Co-Authors: Mohammad Vedady R Moghadam, Rui ZhangAbstract:In multiple-input single-output (MISO) wireless Power transfer (WPT) via magnetic resonant coupling (MRC), multiple transmitters are deployed to enhance the efficiency of Power transfer to the electric load at a single receiver by jointly optimizing their source currents/voltages to constructively combine the induced magnetic fields at the receiver, known as magnetic beamforming. In practice, since the transmitters (Power chargers) are usually at fixed locations and the receiver (e.g. mobile phone) is desired to be freely located in a target region for wireless charging, its received Power can fluctuate significantly over locations even with adaptive magnetic beamforming applied. To achieve uniform coverage, the transmitters need to be optimally placed in the region, which motivates this paper. First, we derive the optimal magnetic beamforming solution in closed-form for a distributed MISO WPT system with given locations of the transmitters and receiver to maximize the deliverable Power to the receiver load subject to a given Sum-Power Constraint at all transmitters. With the optimal magnetic beamforming solution, we then jointly optimize the locations of all transmitters to maximize the minimum Power deliverable to the receiver when it is being moved over a given one-dimensional (1D) region, i.e., a line of finite length. Although the formulated node placement problem is non-convex, we propose an iterative algorithm for solving it efficiently. Extensive simulation results are provided which show the significant performance gains by the proposed design with optimized transmitter locations and magnetic beamforming as compared to other benchmark schemes with non-adaptive or heuristic currents allocation and transmitters placement. Last, we extend the node placement problem to the more general case of two-dimensional (2D) region, and draw the key insights.
-
capacity region of miso broadcast channel for simultaneous wireless information and Power transfer
IEEE Transactions on Communications, 2015Co-Authors: Shixin Luo, Teng Joon Lim, Rui ZhangAbstract:This paper studies a multiple-input–single-output (MISO) broadcast channel (BC) featuring simultaneous wireless information and Power transfer, where a multiantenna access point (AP) delivers both information and energy via radio signals to multiple single-antenna receivers simultaneously, and each receiver implements either information decoding (ID) or energy harvesting (EH). In particular, pseudorandom sequences that are a priori known and therefore can be cancelled at each ID receiver are used as the energy signals, and the information-theoretically optimal dirty paper coding is employed for the information transmission. We characterize the capacity region for ID receivers by solving a sequence of weighted Sum-rate (WSR) maximization (WSRMax) problems subject to a maximum Sum-Power Constraint for the AP, and a set of minimum harvested Power Constraints for individual EH receivers. The problem corresponds to a new form of WSRMax problem in MISO-BC with combined maximum and minimum linear transmit covariance Constraints (MaxLTCCs and MinLTCCs), which differs from the celebrated capacity region characterization problem for MISO-BC under a set of MaxLTCCs only and is challenging to solve. By extending the general BC–multiple-access-channel duality, which is only applicable to WSRMax problems with MaxLTCCs, and applying the ellipsoid method, we propose an efficient iterative algorithm to solve this problem globally optimally. Furthermore, we also propose two suboptimal algorithms with lower complexity by asSuming that the information and energy signals are designed separately. Finally, numerical results are provided to validate our proposed algorithms.
-
capacity region of miso broadcast channel for simultaneous wireless information and Power transfer
arXiv: Information Theory, 2014Co-Authors: Shixin Luo, Teng Joon Lim, Rui ZhangAbstract:This paper studies a multiple-input single-output (MISO) broadcast channel (BC) featuring simultaneous wireless information and Power transfer (SWIPT), where a multi-antenna access point (AP) delivers both information and energy via radio signals to multiple single-antenna receivers simultaneously, and each receiver implements either information decoding (ID) or energy harvesting (EH). In particular, pseudo-random sequences that are {\it a priori} known and therefore can be cancelled at each ID receiver is used as the energy signals, and the information-theoretically optimal dirty paper coding (DPC) is employed for the information transmission. We characterize the capacity region for ID receivers under given energy requirements for EH receivers, by solving a sequence of weighted Sum-rate (WSR) maximization (WSRMax) problems subject to a maximum Sum-Power Constraint for the AP, and a set of minimum harvested Power Constraints for individual EH receivers. The problem corresponds to a new form of WSRMax problem in MISO-BC with combined maximum and minimum linear transmit covariance Constraints (MaxLTCCs and MinLTCCs), which differs from the celebrated capacity region characterization problem for MISO-BC under a set of MaxLTCCs only and is challenging to solve. By extending the general BC-multiple access channel (MAC) duality, which is only applicable to WSRMax problems with MaxLTCCs, and applying the ellipsoid method, we propose an efficient algorithm to solve this problem globally optimally. Furthermore, we also propose two suboptimal algorithms with lower complexity by asSuming that the information and energy signals are designed separately. Finally, numerical results are provided to validate our proposed algorithms.
-
Optimal Power Allocation for Wireless Sensor Networks with Outage Constraint
IEEE Wireless Communications Letters, 2014Co-Authors: Chuan Huang, Rui ZhangAbstract:This letter considers the Power allocation problem for a wireless sensor network, where N distributed sensors transmit delay-limited high rate traffic to a fusion center (FC) via orthogonal channels. The system-level outage event is defined as that the FC can decode none of the messages from the N sensors. We formulate the outage probability minimization problem with a Sum Power Constraint, and show that this problem, albeit being non-convex, possesses an interesting "concave-convex" property in the reformulated form. By exploiting this property, the problem is shown to be solvable optimally via a one-dimension search. Its counterpart problem, i.e., minimizing the Sum Power conSumption with a targeted outage probability, is also solved. Finally, numerical results show that the proposed Power allocation can significantly improve the system performance compared to the conventional uniform Power allocation.
Vincent H Poor - One of the best experts on this subject based on the ideXlab platform.
-
the low bit rate hybrid Power line wireless single relay channel
IEEE Systems Journal, 2019Co-Authors: Victor Fernandes, Weiler A Finamore, Vincent H Poor, Moises V RibeiroAbstract:This paper focuses on the hybrid Power line/wireless single-relay channel (HSRC) model, which is constituted by parallel Power line and wireless single-relay channel (SRC) models, for data communication purposes. Closed-form expressions for ergodic achievable data rates and outage probabilities are presented. These expressions are based on the fact that both Power line and wireless are frequency-selective block-fading channels corrupted by additive random noise with a definite Power spectral density. Moreover, comparisons among HSRC, Power line SRC, and wireless SRC under the Sum Power Constraint with optimal and uniform Power allocations, the use of amplify-and-forward and decode-and-forward cooperative protocols, and different relay positions are discussed. Based on asymptotic, analytical, and numerical results, we show that HSRC outperforms both Power line and wireless SRCs in all considered scenarios. Furthermore, a performance comparison between HSRC and the so-called two wireless SRC shows that the former surpasses the latter when the Sum Power Constraint with optimal Power allocation is adopted.
-
analyses of the incomplete low bit rate hybrid plc wireless single relay channel
IEEE Internet of Things Journal, 2018Co-Authors: Victor Fernandes, Vincent H Poor, Moises V RibeiroAbstract:This paper discusses the ergodic achievable data rate of the so-called incomplete hybrid Power line-wireless single-relay channel (HSRC) model. Here, the term incomplete refers to the HSRC model, which jointly and in parallel uses Power line and wireless single-relay channel (SRC) models, for low-bit-rate data transmission, without a data communication link or a node communication interface. In order to analyze the behavior of such kind of defective HSRC model under the relative location of the relay node in relation to the source and destination nodes, optimal Power allocation is taken into account under a Sum Power Constraint with an amplify-and-forward relaying protocol and maximal ratio combining applied at the relay node. Analytical and numerical results show that the incomplete HSRC model, which is a defective version of the HSRC model, can remarkably outperform Power line and wireless SRCs for all considered positions of the relay node and the chosen cooperative protocols.
-
cooperative in home Power line communication analyses based on a measurement campaign
IEEE Transactions on Communications, 2016Co-Authors: Michelle S P Facina, Vincent H Poor, Haniph A Latchman, Moises V RibeiroAbstract:This work focuses on analyses of cooperative protocols to enhance the performance of Power line communication systems. Based on a measurement campaign and considering a Sum Power Constraint, achievable data rates for amplify-and-forward (AF) and decode-and-forward (DF) protocols are analyzed. Similar investigations are performed for the maximum data rates attained using Hermitian-symmetric orthogonal frequency division multiplexing (HS-OFDM) together with equal gain combining (EGC), selection combining (SC) and maximal ratio combining (MRC) techniques. The influences of optimally and uniformly allocated transmission Power and frequency bandwidth are are also analyzed and the efficiency of combination before and after equalization is compared. Results show that the relative distances among source, relay and destination nodes significantly impact system performance. Also, they reveal a range of total transmission Power and bandwidth in which benefits can be verified. Among combining techniques, MRC and SC present similar results, but MRC offers a slightly better performance. In relation to computational complexity, SC is the most favorable. Maximum data rate analyses of HS-OFDM with frequency domain equalization based on zero forcing and minimum mean square error criteria show that the former scheme offers almost the same performance as the latter. Furthermore, it is shown that equalization after combination is more advantageous.
-
on gaussian mimo bc mac duality with multiple transmit covariance Constraints
IEEE Transactions on Information Theory, 2012Co-Authors: Lan Zhang, Rui Zhang, Yingchang Liang, Yan Xin, Vincent H PoorAbstract:Owing to the special structure of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC), the associated capacity region computation and beamforming optimization problems are typically non-convex, and thus cannot be solved directly. One feasible approach is to consider the respective dual multiple-access channel (MAC) problems, which are easier to deal with due to their convexity properties. The conventional BC-MAC duality has been established via BC-MAC signal transformation, and is applicable only for the case in which the MIMO BC is subject to a single transmit Sum-Power Constraint. An alternative approach is based on minimax duality, which can be applied to the case of the Sum-Power Constraint or per-antenna Power Constraint. In this paper, the conventional BC-MAC duality is extended to the general linear transmit covariance Constraint (LTCC) case, which includes Sum-Power and per-antenna Power Constraints as special cases. The obtained general BC-MAC duality is applied to solve the capacity region computation for the MIMO BC and beamforming optimization for the multiple-input single-output (MISO) BC, respectively, with multiple LTCCs. The relationship between this new general BC-MAC duality and the minimax duality is also discussed, and it is shown that the general BC-MAC duality leads to simpler problem formulations. Moreover, the general BC-MAC duality is extended to deal with the case of nonlinear transmit covariance Constraints in the MIMO BC.
-
on gaussian mimo bc mac duality with multiple transmit covariance Constraints
International Symposium on Information Theory, 2009Co-Authors: Lan Zhang, Rui Zhang, Yingchang Liang, Yan Xin, Vincent H PoorAbstract:The conventional Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC)- multiple-access channel (MAC) duality has previously been applied to solve non-convex BC capacity computation problems. However, this conventional duality approach is applicable only to the case in which the base station (BS) of the BC is subject to a single Sum-Power Constraint. An alternative approach is the minimax duality, established by Yu in the framework of Lagrange duality, which can be applied to solve the per-antenna Power Constraint case. This paper first extends the conventional BC-MAC duality to the general linear transmit covariance Constraint (LTCC) case, and thereby establishes a general BC-MAC duality. This new duality is then applied to solve the BC capacity computation problem with multiple LTCCs. Moreover, the relationship between this new general BC-MAC duality and the minimax duality is also presented, and it is shown that the general BC-MAC duality has a simpler form. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.
Yingchang Liang - One of the best experts on this subject based on the ideXlab platform.
-
on gaussian mimo bc mac duality with multiple transmit covariance Constraints
IEEE Transactions on Information Theory, 2012Co-Authors: Lan Zhang, Rui Zhang, Yingchang Liang, Yan Xin, Vincent H PoorAbstract:Owing to the special structure of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC), the associated capacity region computation and beamforming optimization problems are typically non-convex, and thus cannot be solved directly. One feasible approach is to consider the respective dual multiple-access channel (MAC) problems, which are easier to deal with due to their convexity properties. The conventional BC-MAC duality has been established via BC-MAC signal transformation, and is applicable only for the case in which the MIMO BC is subject to a single transmit Sum-Power Constraint. An alternative approach is based on minimax duality, which can be applied to the case of the Sum-Power Constraint or per-antenna Power Constraint. In this paper, the conventional BC-MAC duality is extended to the general linear transmit covariance Constraint (LTCC) case, which includes Sum-Power and per-antenna Power Constraints as special cases. The obtained general BC-MAC duality is applied to solve the capacity region computation for the MIMO BC and beamforming optimization for the multiple-input single-output (MISO) BC, respectively, with multiple LTCCs. The relationship between this new general BC-MAC duality and the minimax duality is also discussed, and it is shown that the general BC-MAC duality leads to simpler problem formulations. Moreover, the general BC-MAC duality is extended to deal with the case of nonlinear transmit covariance Constraints in the MIMO BC.
-
on gaussian mimo bc mac duality with multiple transmit covariance Constraints
International Symposium on Information Theory, 2009Co-Authors: Lan Zhang, Rui Zhang, Yingchang Liang, Yan Xin, Vincent H PoorAbstract:The conventional Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC)- multiple-access channel (MAC) duality has previously been applied to solve non-convex BC capacity computation problems. However, this conventional duality approach is applicable only to the case in which the base station (BS) of the BC is subject to a single Sum-Power Constraint. An alternative approach is the minimax duality, established by Yu in the framework of Lagrange duality, which can be applied to solve the per-antenna Power Constraint case. This paper first extends the conventional BC-MAC duality to the general linear transmit covariance Constraint (LTCC) case, and thereby establishes a general BC-MAC duality. This new duality is then applied to solve the BC capacity computation problem with multiple LTCCs. Moreover, the relationship between this new general BC-MAC duality and the minimax duality is also presented, and it is shown that the general BC-MAC duality has a simpler form. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.
-
weighted Sum rate optimization for cognitive radio mimo broadcast channels
IEEE Transactions on Wireless Communications, 2009Co-Authors: Yingchang LiangAbstract:In this paper, we consider a cognitive radio (CR) network, in which the unlicensed (secondary) users are allowed to concurrently access the spectrum allocated to the licensed (primary) users provided that their interference to the primary users (PUs) satisfies certain Constraints. We study a weighted Sum rate maximization problem for the secondary user (SU) multiple input multiple output (MIMO) broadcast channel (BC), in which the SUs are subject to not only a Sum Power Constraint but also interference Power Constraints. We transform this multiConstraint maximization problem into its equivalent form, which involves a single Constraint with multiple auxiliary variables. Fixing these multiple auxiliary variables, we propose a duality result for the equivalent problem. Exploiting the duality result, we develop an efficient subgradient based iterative algorithm to solve the equivalent problem and show that the developed algorithm converges to a globally optimal solution. Simulation results are provided to corroborate the effectiveness of the proposed algorithm.
-
on gaussian mimo bc mac duality with multiple transmit covariance Constraints
arXiv: Information Theory, 2008Co-Authors: Lan Zhang, Rui Zhang, Yingchang Liang, Yan Xin, Vincent H PoorAbstract:Owing to the structure of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC), associated optimization problems such as capacity region computation and beamforming optimization are typically non-convex, and cannot be solved directly. One feasible approach to these problems is to transform them into their dual multiple access channel (MAC) problems, which are easier to deal with due to their convexity properties. The conventional BC-MAC duality is established via BC-MAC signal transformation, and has been successfully applied to solve beamforming optimization, signal-to-interference-plus-noise ratio (SINR) balancing, and capacity region computation. However, this conventional duality approach is applicable only to the case, in which the base station (BS) of the BC is subject to a single Sum Power Constraint. An alternative approach is minimax duality, established by Yu in the framework of Lagrange duality, which can be applied to solve the per-antenna Power Constraint problem. This paper extends the conventional BC-MAC duality to the general linear Constraint case, and thereby establishes a general BC-MAC duality. This new duality is applied to solve the capacity computation and beamforming optimization for the MIMO and multiple-input single-output (MISO) BC, respectively, with multiple linear Constraints. Moreover, the relationship between this new general BC-MAC duality and minimax duality is also presented. It is shown that the general BC-MAC duality offers more flexibility in solving BC optimization problems relative to minimax duality. Numerical results are provided to illustrate the effectiveness of the proposed algorithms.
-
weighted Sum rate optimization for cognitive radio mimo broadcast channels
International Conference on Communications, 2008Co-Authors: Yingchang LiangAbstract:In this paper, we consider a cognitive radio (CR) network in which the unlicensed (secondary) users (SUs) are allowed to concurrently access the spectrum allocated to the licensed (primary) users provided that their interference to the primary users (PUs) satisfies certain Constraints. We study a weighted Sum rate maximization problem for the secondary user (SU) multiple input multiple output (MIMO) broadcast channel (BC), in which the SUs have not only the Sum Power Constraint but also interference Constraints. We first transform this multi- Constraint maximization problem into its equivalent form, which involves a single Constraint with multiple auxiliary variables. Fixing these multiple auxiliary variables, we establish a duality result for the equivalent problem. Our duality result can be viewed as an extension of the previously known results, which depend on either a Sum Power Constraint or per-antenna Power Constraints. Furthermore, we develop an efficient sub-gradient based iterative algorithm to solve the equivalent problem and show that the developed algorithm converges to a globally optimal solution. Computer simulations are also provided to corroborate the effectiveness of the proposed algorithm.
Moises V Ribeiro - One of the best experts on this subject based on the ideXlab platform.
-
the low bit rate hybrid Power line wireless single relay channel
IEEE Systems Journal, 2019Co-Authors: Victor Fernandes, Weiler A Finamore, Vincent H Poor, Moises V RibeiroAbstract:This paper focuses on the hybrid Power line/wireless single-relay channel (HSRC) model, which is constituted by parallel Power line and wireless single-relay channel (SRC) models, for data communication purposes. Closed-form expressions for ergodic achievable data rates and outage probabilities are presented. These expressions are based on the fact that both Power line and wireless are frequency-selective block-fading channels corrupted by additive random noise with a definite Power spectral density. Moreover, comparisons among HSRC, Power line SRC, and wireless SRC under the Sum Power Constraint with optimal and uniform Power allocations, the use of amplify-and-forward and decode-and-forward cooperative protocols, and different relay positions are discussed. Based on asymptotic, analytical, and numerical results, we show that HSRC outperforms both Power line and wireless SRCs in all considered scenarios. Furthermore, a performance comparison between HSRC and the so-called two wireless SRC shows that the former surpasses the latter when the Sum Power Constraint with optimal Power allocation is adopted.
-
analyses of the incomplete low bit rate hybrid plc wireless single relay channel
IEEE Internet of Things Journal, 2018Co-Authors: Victor Fernandes, Vincent H Poor, Moises V RibeiroAbstract:This paper discusses the ergodic achievable data rate of the so-called incomplete hybrid Power line-wireless single-relay channel (HSRC) model. Here, the term incomplete refers to the HSRC model, which jointly and in parallel uses Power line and wireless single-relay channel (SRC) models, for low-bit-rate data transmission, without a data communication link or a node communication interface. In order to analyze the behavior of such kind of defective HSRC model under the relative location of the relay node in relation to the source and destination nodes, optimal Power allocation is taken into account under a Sum Power Constraint with an amplify-and-forward relaying protocol and maximal ratio combining applied at the relay node. Analytical and numerical results show that the incomplete HSRC model, which is a defective version of the HSRC model, can remarkably outperform Power line and wireless SRCs for all considered positions of the relay node and the chosen cooperative protocols.
-
cooperative in home Power line communication analyses based on a measurement campaign
IEEE Transactions on Communications, 2016Co-Authors: Michelle S P Facina, Vincent H Poor, Haniph A Latchman, Moises V RibeiroAbstract:This work focuses on analyses of cooperative protocols to enhance the performance of Power line communication systems. Based on a measurement campaign and considering a Sum Power Constraint, achievable data rates for amplify-and-forward (AF) and decode-and-forward (DF) protocols are analyzed. Similar investigations are performed for the maximum data rates attained using Hermitian-symmetric orthogonal frequency division multiplexing (HS-OFDM) together with equal gain combining (EGC), selection combining (SC) and maximal ratio combining (MRC) techniques. The influences of optimally and uniformly allocated transmission Power and frequency bandwidth are are also analyzed and the efficiency of combination before and after equalization is compared. Results show that the relative distances among source, relay and destination nodes significantly impact system performance. Also, they reveal a range of total transmission Power and bandwidth in which benefits can be verified. Among combining techniques, MRC and SC present similar results, but MRC offers a slightly better performance. In relation to computational complexity, SC is the most favorable. Maximum data rate analyses of HS-OFDM with frequency domain equalization based on zero forcing and minimum mean square error criteria show that the former scheme offers almost the same performance as the latter. Furthermore, it is shown that equalization after combination is more advantageous.
Lan Zhang - One of the best experts on this subject based on the ideXlab platform.
-
on gaussian mimo bc mac duality with multiple transmit covariance Constraints
IEEE Transactions on Information Theory, 2012Co-Authors: Lan Zhang, Rui Zhang, Yingchang Liang, Yan Xin, Vincent H PoorAbstract:Owing to the special structure of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC), the associated capacity region computation and beamforming optimization problems are typically non-convex, and thus cannot be solved directly. One feasible approach is to consider the respective dual multiple-access channel (MAC) problems, which are easier to deal with due to their convexity properties. The conventional BC-MAC duality has been established via BC-MAC signal transformation, and is applicable only for the case in which the MIMO BC is subject to a single transmit Sum-Power Constraint. An alternative approach is based on minimax duality, which can be applied to the case of the Sum-Power Constraint or per-antenna Power Constraint. In this paper, the conventional BC-MAC duality is extended to the general linear transmit covariance Constraint (LTCC) case, which includes Sum-Power and per-antenna Power Constraints as special cases. The obtained general BC-MAC duality is applied to solve the capacity region computation for the MIMO BC and beamforming optimization for the multiple-input single-output (MISO) BC, respectively, with multiple LTCCs. The relationship between this new general BC-MAC duality and the minimax duality is also discussed, and it is shown that the general BC-MAC duality leads to simpler problem formulations. Moreover, the general BC-MAC duality is extended to deal with the case of nonlinear transmit covariance Constraints in the MIMO BC.
-
on gaussian mimo bc mac duality with multiple transmit covariance Constraints
International Symposium on Information Theory, 2009Co-Authors: Lan Zhang, Rui Zhang, Yingchang Liang, Yan Xin, Vincent H PoorAbstract:The conventional Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC)- multiple-access channel (MAC) duality has previously been applied to solve non-convex BC capacity computation problems. However, this conventional duality approach is applicable only to the case in which the base station (BS) of the BC is subject to a single Sum-Power Constraint. An alternative approach is the minimax duality, established by Yu in the framework of Lagrange duality, which can be applied to solve the per-antenna Power Constraint case. This paper first extends the conventional BC-MAC duality to the general linear transmit covariance Constraint (LTCC) case, and thereby establishes a general BC-MAC duality. This new duality is then applied to solve the BC capacity computation problem with multiple LTCCs. Moreover, the relationship between this new general BC-MAC duality and the minimax duality is also presented, and it is shown that the general BC-MAC duality has a simpler form. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.
-
on gaussian mimo bc mac duality with multiple transmit covariance Constraints
arXiv: Information Theory, 2008Co-Authors: Lan Zhang, Rui Zhang, Yingchang Liang, Yan Xin, Vincent H PoorAbstract:Owing to the structure of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC), associated optimization problems such as capacity region computation and beamforming optimization are typically non-convex, and cannot be solved directly. One feasible approach to these problems is to transform them into their dual multiple access channel (MAC) problems, which are easier to deal with due to their convexity properties. The conventional BC-MAC duality is established via BC-MAC signal transformation, and has been successfully applied to solve beamforming optimization, signal-to-interference-plus-noise ratio (SINR) balancing, and capacity region computation. However, this conventional duality approach is applicable only to the case, in which the base station (BS) of the BC is subject to a single Sum Power Constraint. An alternative approach is minimax duality, established by Yu in the framework of Lagrange duality, which can be applied to solve the per-antenna Power Constraint problem. This paper extends the conventional BC-MAC duality to the general linear Constraint case, and thereby establishes a general BC-MAC duality. This new duality is applied to solve the capacity computation and beamforming optimization for the MIMO and multiple-input single-output (MISO) BC, respectively, with multiple linear Constraints. Moreover, the relationship between this new general BC-MAC duality and minimax duality is also presented. It is shown that the general BC-MAC duality offers more flexibility in solving BC optimization problems relative to minimax duality. Numerical results are provided to illustrate the effectiveness of the proposed algorithms.
